Method and apparatus for compressing multispectral images

ABSTRACT

The multispectral image compressing method performs logarithmic conversion of a multispectral image and segmentation of the multispectral image into a plurality of tile images to obtain logarithmically converted tile image data, performs principal component analysis on the logarithmically converted tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image, determines from the plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image, thereby, the image data for the multispectral image is compressed to the optimum principal component number, the optimum principal component images and optimum principal component vectors for each tile image.

BACKGROUND OF THE INVENTION

This invention relates to an image data compressing technology by which image data for a multispectral image reconstructed from a plurality of band images of a photographed subject captured in a wavelength range as divided into a plurality of bands can be efficiently compressed without impairing the quality of the image.

With the recent advances in digital image processing, it has become possible to provide a complete representation of image's color information (lightness, hue and saturation) by a multispectral image which carries spectral information for each of its pixels. To produce a multispectral image, a subject is recorded in divided wavelength bands and on the basis of a multiband image consisting of the captured band images, the spectral reflectance distribution is estimated for each band image. The multiband image can reproduce the color information that cannot be fully represented by the conventional RGB color images consisting of red (R), green (G) and blue (B) images and will prove effective in the art world which needs more accurate color reproduction. In order to make the most of the capability for correct reproduction of color information, the multispectral image is desirably produced from a multiband image having as many bands as possible, say, 41 bands which are obtained by dividing a recording wavelength range of from 380 to 780 nm by 10 nm or even 81 bands by dividing the range by 5 nm.

A problem with the multispectral image having spectral information for each pixel is that it possesses spectral reflectance data for each group of the channels, say, 41 channels, into which the recording wavelength range has been divided and that compared to the conventional 3-channel RGB color images, the volume of the required image data is about 13 times as much (41/3). This means a large storage capacity and a prolonged time are required to save the image data for multispectral images. In addition, so much time is taken to transfer the image data over a communication network that great difficulty is involved in data handling.

To deal with this situation, Keusen proposed an improved technique in which spectral waveforms obtained from spectral information for each of the pixels in a multispectral image were expanded into three color matching functions as of the RGB calorimetric system and those portions of the spectral waveforms that could not be expressed by color matching functions were expanded into basis vectors by principal component analysis and those principal components which were representative of the image information for the spectral images were extracted and adopted but other principal components were discarded so that the spectral waveforms of interest were eventually expressed by a total of six to eight basis vectors including the color matching functions [Th. Keusen, Multispectral Color System with an Encoding Format Compatible with the Conventional Tristimulus Model, Journal of Imaging Science and Technology 40: 510 to 515, (1996)]. In accordance with this method, the image data for the multispectral image can be compressed by expressing the spectral waveforms in terms of six to eight basis vectors and their multispectral coefficients. In particular, coefficients of the color matching functions expressed by those of the RGB calorimetric system become R, G and B tristimulus values whereby the image can be directly sent for subsequent processing without performing any special transformations in order to provide compatibility for the conventional image processing and display apparatus which employ the tristimulus values of R, G and B pixels. This is a great benefit to the purpose of reducing the volume of data processing.

If a multispectral image consisting of 41 spectral images is expressed by eight basis vectors and their multispectral coefficients according to the method described above, the image data to be handled can be compressed to about 20% of the initial volume (8/41×100). Compared to the RGB color image, the volume of the image data for the multispectral image composed of 41 spectral images is about 13 times as much and even if it is reduced to about 20% of the initial value by the Keusen method, the volume is still as much as about 2.5 times the image data for the RGB color image (13×20/100). As a result, the aforementioned problem of the prior art still remains and the time for recording and storing the image data on recording media as well as the time for transferring them over a communication network are long enough to present difficulty in data handling.

SUMMARY OF THE INVENTION

The present invention has been accomplished under these circumstances and has as an object providing a method by which a multispectral image reconstructed from a plurality of spectral images obtained by recording a subject in a wavelength range as divided into a plurality of bands can be compressed at an increased ratio but without causing visual deterioration and which thereby improves the efficiency of image data handling.

Another object of the invention is to provide an apparatus for implementing the method.

In order to attain the object described above, the present invention provides a method for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising the steps of performing logarithmic conversion of the multispectral image and segmentation of the multispectral image into a plurality of tile images to obtain logarithmically converted tile image data; performing principal component analysis on the logarithmically converted tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image; determining from the plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image; and expressing compressed image data for the multispectral image by means of at least the optimum principal component number of sets of optimum principal component images and optimum principal component vectors for each tile image.

It is preferable that the logarithmically converted tile image data is obtained by segmenting the multispectral image into the plurality of tile images and subjecting image data of each of the plurality of tile images to logarithmic conversion, the logarithmically converted tile image data is obtained by subjecting the multispectral image to logarithmic conversion to obtain logarithmically converted image data of the multispectral image and segmenting the multispectral image into the plurality of tile images based on the logarithmically converted image data, or the logarithmically converted tile image data is determined based on logarithmically converted image data of the multispectral image obtained by subjecting the multispectral image to logarithmic conversion and segmentation information for segmenting the multispectral image into a plurality of tile images.

It is further preferable that the compressed image data for the multispectral image are expressed not only by the optimum principal component number of sets of the optimum principal component images and the optimum principal component vectors but also by tile image information having information about tile numbers of the tile images, a tile position and an image size of the tile images.

It is also preferable that the optimum principal component number is determined based on colorimetric values in a color space.

It is another preferable that the optimum principal component number is a minimum principal component number at which an absolute value of difference between image information about a composite image that is composed of selected number of sets of the principal component vectors and the principal component images and the image information about an original image that is composed based on the multispectral image is below a specified value.

It is also further preferable that the optimum principal component number is a minimum principal component number at which an absolute value of variation in error from an original image that is composed based on the multispectral image which is presented when a composite image that is composed of selected number of sets of the principal component vectors and the principal component images is determined by sequentially including in an order of contribution those principal component vectors which make greater contribution to the multispectral image does not exceed a predetermined value.

It is still another preferable that an image size of the tile images in terms of pixel is expressed as a power notation of 2 in both a longitudinal and a transverse direction.

It is still further preferable that the tile images all have an image size in terms of pixel.

In order to attain another object described above, the present invention provides an apparatus for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising an image segmenting section for segmenting the multispectral image or a logarithmically converted multispectral image into a plurality of tile images, or obtaining segmentation information for segmenting the multispectral image into the plurality of tile images; an image data converting section which logarithmically converts image data of the multispectral image or the plurality of tile images obtained by the image segmenting section; a principal component analyzing section in which respective logarithmically converted tile image data obtained via the image segmenting section and the image data converting section are subjected for each tile image to principal component analysis to obtain a principal component number of sets of principal component vectors and principal component images for the multispectral image; and an optimum principal component vector/image extracting section which determines an optimum principal component number of sets of optimum principal component vectors and optimum principal component images corresponding to the principal component number of sets of principal component vectors and principal component images that optimally represent image information about the multispectral image from the principal component number of sets of principal component vectors and principal component images as obtained in the principal component analyzing section; and wherein image data for the multispectral image is compressed by expressing by means of at least the optimum principal component vectors and the image data for the optimum principal component images for each tile image as obtained in the optimum principal component vector/image extracting section.

It is preferable that the image segmenting section segments the multispectral image into the plurality of tile images and the image data converting section converts logarithmically the image data of the plurality of tile images obtained by the image segmenting section to obtain the logarithmically converted tile image data.

It is also preferable that the image data converting section converts logarithmically the image data of the multispectral image to obtain logarithmically converted image data of the multispectaral image and the image segmenting section segments logarithmically converted multispectral image into the plurality of tile images based on the logarithmically converted image data of the multispectral image to obtain the logarithmically converted tile image data.

It is another preferable that the image segmenting section obtains segmentation information for segmenting the multispectral image into the plurality of tile images, the image data converting section converts logarithmically the image data of the multispectral image to obtain logarithmically converted image data of the multispectaral image and the principal component analyzing section determines the logarithmically converted tile image data based on the segmentation information obtained by the image segmenting section and the logarithmically converted image data of the multispectral image obtained by the image data converting section before the principal component analysis.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows in conceptual form an exemplary multispectral image acquisition system including the multispectral image compressing apparatus according to the second aspect of the invention;

FIG. 2 is a block diagram for an example of the multispectral image compressing apparatus;

FIG. 3 illustrates how an image is segmented into tile images in the practice of the multispectral image compressing method according to the first aspect of the invention; and

FIG. 4 is a flowchart for an example of the multispectral image compressing method.

THE PREFERRED EMBODIMENT OF THE INVENTION

A multispectral image acquisition system for implementing the multispectral image compressing method according to the first aspect of the invention is described below in detail by reference to the preferred embodiment shown in the accompanying drawings.

FIG. 1 shows a multispectral image acquisition system for implementing the multispectral image compressing method according to the first aspect of the invention and which includes the multispectral image compressing apparatus according to the second aspect of the invention.

The multispectral image acquisition system (hereunder referred to simply as “the system”) which is generally indicated by 10 takes a multispectral image of an subject O and stores the captured image data on a recording medium. Its principal components are as follows: a light source 12 for illuminating the subject O; a variable filter 14 for dividing the recording wavelength range into a plurality of bands; a CCD camera 16 for taking a multiband image of the subject O; a multiband image data storage unit 18 for saving the captured image data temporarily; a multispectral image acquisition unit 20 for reconstructing a multispectral image from the multiband image by estimating the spectral reflectance distribution for each pixel; a multispectral image compressing unit 22 for compressing the image data for the multispectral image at a higher ratio while suffering limited visual deterioration; and a recording medium drive unit 24 for saving the compressed image data. In the present invention, the multispectral image has preferably at least six channels of spectral images and, hence, at least six constituent wavelengths in the spectral reflectance distribution.

The light source 12 for illuminating the subject O is not limited in any particular way as regards type and other factors; however, for the purpose of reconstructing a multispectral image from the captured multiband image by estimating the spectral reflectance distribution, the light source 12 is preferably one with a known spectral intensity distribution.

The variable filter 14 is a bandpass filter with which the recording wavelength range can be divided into a variable number of bands, say, 16, 21, 41, 81, 201 bands or the like for the purpose of capturing a multiband image of the subject O. A useful example of the variable filter 14 is a liquid-crystal tunable filter.

The CCD camera 16 is a device with which the subject O imaged by the light reflected from it to pass through the variable filter 14 so that it is segmented into a desired number of spectral wavelength bands is captured as black-and-white band images. The image-receiving plane of this camera has a planar array of CCDs (charge-coupled devices) as area sensors. For proper setting of the dynamic range for the lightness values of the image to be taken, the CCD camera 16 includes a mechanism for adjusting the white balance before taking the picture of the subject O.

The multiband image data storage unit 18 is a site for temporary storage and saving of the multiband image composed of a plurality of band images that have been captured in the recording wavelength range as divided into multiple bands and which have the white balance adjusted in association with the respective bands.

The multispectral image acquisition unit 20 has one-dimensional look-up tables (1D LUTs) constructed preliminarily on the basis of the correspondence between the image data for subjects with known values of spectral reflectance that were captured with the CCD camera 16, for example, the image data for the gray patches on a Macbeth chart, and the known values of spectral reflectance. Using the 1D-LUTs and on the basis of the image data for the multiband image of the subject O that have been retrieved from the multiband image data storage unit 18, the multispectral image acquisition unit 20 estimates the spectral reflectance of the subject O for each pixel and acquires the multispectral image.

If the variable filter 14 has such filter characteristics that its spectral transmittance distribution partly overlaps between bands, the acquired multispectral image does not have a sharp enough spectral reflectance distribution to assure highly precise estimation of the spectral reflectance distribution of the subject O. To avoid this problem, such filter characteristics may be eliminated by deconvolution through matrix operations and Fourier transformation.

In this way, the multispectral image having an n-wavelength spectral reflectance distribution as spectral information is acquired from the multiband image captured with the aid of the variable filter 14 that divided the recording wavelength range into n bands.

The recording medium drive unit 24 records the image data on various kinds of recording media such as hard disk, floppy disk, MO, CD-R and DVD after they have been compressed by the multispectral image compressing unit 22 to be described later. In addition to or in place of the recording medium drive unit 24, a network coupler may be installed so that the compressed multispectral image data to be described later can be transferred over a variety of communication networks.

The multispectral image compressing unit 22 is a site which determines image data compressed at high ratio while suffering only limited visual deterioration from the multispectral image data that constitutes the multispectral image obtained by the multispectral image acquisition unit 20. As shown in FIG. 2, the multispectral image compressing unit 22 comprises an image data converting section 22 a, an image segmenting section 22 b, a principal component analyzing section 22 c and an optimum principal component vector/image extracting section 22 d. If desired, the multispectral image compressing unit 22 may be composed of software having the capabilities set forth below or it may be configured as a single hardware unit.

The image data converting section 22 a is a site where the image data of the multispectral image sent from the multispectral image acquisition unit 20 is subjected to a logarithmic conversion (hereinafter referred to as log conversion) to obtain logarithmically converted (hereinafter referred to as log-converted) image data which is, thereafter, sent to the principal component analyzing section 22 c. On this occasion, the log conversion is performed by using a known converting device such as a one-dimensional look-up table and the like.

Log conversion of image data allows the compression ratio of images to be enhanced, as described below.

The image segmenting section 22 b is a site where the image (image data of which is log-converted by the image data converting section 22 a) obtained by the multispectral image acquisition unit 20 is segmented into n_(t) tile images T_(L) (L=1 to n_(t)) and the resultant segmentation information is sent to the principal component analyzing section 22 c. To give just one example, a multispectral image consisting of 1024×1024 pixels may be segmented into tile images T_(L) each consisting of 16×16 pixels. The size of tile images T_(L) must be such that their total number of the pixels in each tile image T_(L) is at least greater than the number of constituent wavelengths in the spectral reflectance distribution for each of the pixels in the multispectral image. If this condition is not met, highly precise principal component vectors cannot be determined in a principal component analysis to be discussed below.

FIG. 3 shows an example of segmenting the multispectral image into tile images T_(L). In the illustrated case, the multispectral image is segmented in eight rows and eight columns to obtain 64 tile images T_(L) of identical size. Tile images of identical size permit easy processing in the analysis of principal components and the construction of image information that are to be discussed later. Preferably, the longitudinal and transverse sizes of each tile image T_(L) are such that the number of pixels can be expressed as a power notation of 2. This is in order to provide convenience for retrieving the compressed image data and performing necessary processing.

In the embodiment under discussion, all tile images T_(L) are of the same size but this is not the sole case of the invention and the size of tile images T_(L) may be varied as appropriate for the contents of the image. For example, if the background of the image is the sky, the ground or the like that has small changes in hue, lightness or saturation, the size of the tile images T_(L) in such background may be increased; on the other hand, the tile images T_(L) in a portion that includes the subject and the like which has marked changes in hue, lightness or saturation may have a smaller size. Varying the tile image size contributes to an enhanced compression ratio.

As long as the total number of pixels in each tile image T_(L) is at least greater than the number of constituent wavelengths, the size of the tile image T_(L), its aspect ratio and other factors are not limited in any particular way.

The principal component analyzing section 22 c is a site where the log-converted tile image data of the segmented tile images T_(L) are obtained on the basis of both of the segmentation information sent from the image segmenting section 22 b and the log-converted image data sent from the image data converting section 22 a, the thus obtained log-converted tile image data are subjected to the principal component analysis and, then, spectral reflectance distribution is expanded into the principal components in log-converted form. In the following discussion, the number of wavelength bands is assumed to be n.

The principal component analysis to be performed in the present invention is commonly called “optimum sampling” which is one of the methods for sampling a group of observed waveform data by expansion into orthonormalized basis functions. This is a method for providing the most precise representation of the observed waveform data by weighted averaging of the least number of orthogonal basis functions. In the following discussion, the orthogonal basis functions are called “principal component vectors”.

A specific procedure for the principal component analysis to be performed in the present invention is this. On the basis of the observed waveforms for the log-converted tile image data of the tile images T_(L) (L=1 to n_(t)) into which the multispectral image Ms has been segmented, linearly independent eigenvectors that are inherent in the observed waveforms are determined as principal component vectors by statistical and eigenvalue analyzing techniques. From the thus determined principal component vectors, those principal component vectors having such small eigenvalues that would be reduced to zero if the observed waveforms had no noise component are eliminated to determine a smaller number of optimum principal component vectors than n, or the number of wavelength bands. The observed waveforms are linearly expressed by these optimum principal component vectors. For details of this method, see “Kagaku Keisoku no tameno Hakei Data Shori (Waveform Data Processing for Scientific Measurement and Instrumentation)”, ed. and written by S. Minami, pp. 220 to 225, CQ Publishing Co., 1986. The principal component analysis is primarily performed in the principal component analyzing section 22 c and the optimum principal component vector/image extracting section 22 d to be described later.

The principal component analysis is preferably performed in the case where the noise component in spectral reflectance waveform is also independent of the value of spectral reflectance.

We now describe the procedure of principal component analysis with particular reference being made to the embodiment under consideration. The log-converted tile image data of the tile images T_(L) (L=1 to n_(t)) into which the multispectral image has been segmented have the number of log-converted values of spectral reflectance distribution equal to the number (n) of bands obtained by segmenting a wavelength band region in which the subject is taken by the variable filter 14. To be more specific, the log-converted tile image data have the logarithmic value which has n constituent wavelengths corresponding to n bands and which consists of n values of spectral reflectance. Further, the total number of pixels in the tile images T_(L) is greater than n, or the number of constituent wavelengths. Hence, by means of a statistical treatment that is independent of the pixel position in the images, namely, by expressing the log-converted tile image data R(i,j,λ) of the spectral reflectance distribution of the tile images T_(L) in the form of an n-dimensional vector, i.e. [R(i,j,λ₁), R(i,j,λ₂), R(i,j,λ₃), . . . , R(i,j,λ_(n))]^(T) (superscript T represents transposition, (i,j) the position of a pixel of interest, and λ the spectral wavelength) and determining an autocorrelation matrix T of this R for all pixels in the tile images T_(L), principal component analysis of the spectral reflectance can be performed statistically.

The principal components to be determined by principal component analysis are obtained by statistical treatments. To give an example, principal component vectors p_(k)(λ) (k is an integer between 1 and n) which are eigenvectors of an orthonormalized autocorrelation matrix T consisting of n log-converted spectral reflectance values corresponding to n bands are determined as a plurality of sets with eigenvalues u_(k) (k=1 to n) of the autocorrelation matrix T. The principal component vectors p_(k)(λ) (k=1 to n) are used to obtain a linear expansion of the log-converted tile image data of the spectral reflectance distribution R(i,j,λ) for the tile images T_(L) at pixel position (i,j), as well as to determine the coefficients s_(k)(i,j) (k=1 to n) for the respective principal component vectors p_(k)(λ) (k=1 to n); thereafter, principal component images S_(k) (k=1 to n) are obtained that use those coefficients as image data at the pixel position (i,j).

It is possible to determine the (i,j) component of the T matrix using the spectral reflectance distribution R(i,j,λ) (column vector or matrix with n rows and one column, namely a column matrix). In this operation, the spectral reflectance distribution R(i,j,λ) can be expressed by the following formula:

$\begin{matrix} {{R\left( {i,j,\lambda} \right)} = \left( {{R\left( {i,j,\lambda_{1}} \right)},{{R\left( {i,j,\lambda_{2}} \right)}\cdots}\mspace{14mu},{R\left( {i,j,\lambda_{n}} \right)}} \right)^{T}} \\ {= \left( \begin{matrix} {R\left( {i,j,\lambda_{1}} \right)} \\ {R\left( {i,j,\lambda_{2}} \right)} \\ \cdots \\ {R\left( {i,j,\lambda_{n}} \right)} \end{matrix} \right)} \end{matrix}$ where R(i,j,λ₁) denotes a scalar and the superscript “T” denotes transpose.

Therefore, the (i,j) component of the T matrix can be expressed by the following equation:

$\begin{matrix} {{T\left( {i,j} \right)} = {{R\left( {i,j,\lambda} \right)}^{T}{{R\left( {i,j,\lambda} \right)}/n}}} \\ {= {{1/{n\left( {{R\left( {i,j,\lambda_{1}} \right)},{R\left( {i,j,\lambda_{2}} \right)},\cdots\mspace{14mu},{R\left( {i,j,\lambda_{n}} \right)}} \right)}}\left( \begin{matrix} {R\left( {i,j,\lambda_{1}} \right)} \\ {R\left( {i,j,\lambda_{2}} \right)} \\ \vdots \\ {R\left( {i,j,\lambda_{n}} \right)} \end{matrix} \right)}} \\ {= {\left( {{{R\left( {i,j,\lambda_{1}} \right)}^{2} + {R\left( {i,j,\lambda_{2}} \right)}^{2} +},\cdots\mspace{14mu},{+ {R\left( {i,j,\lambda_{n}} \right)}^{2}}} \right)/n}} \end{matrix}$

Principal component analysis is performed on the respective tile images T_(L) (L=1 to n_(t)) and the principal component vectors p_(k)(λ) (k=1 to n) of the log-converted tile image data R(i,j,λ) and the principal component images S_(k) (k=1 to n) that have been obtained for each tile image T_(L) (L=1to n_(t)) are sent to the optimum principal component vector/image extracting section 22 d.

In the optimum principal component vector/image extracting section 22 d, the principal component vectors p_(k)(λ) (k=1 to n) of the log-converted tile image data R(i,j,λ) for each tile image T_(L) (hereinafter referred to as principal component vector p_(k)(λ) for tile image T_(L)) obtained in the principal component analyzing section 22 c and the corresponding principal component images S_(k) (k=1 to n) are used to determine an optimum principal component number m₁ for each tile image T_(L) (L=1 to n_(t)), which is used to extract optimum principal component vectors p_(k)(λ) (k=1 to m₁) and the optimum principal component images S_(k) (k=1 to m₁) for each tile image T_(L) (L=1 to n_(t)).

To be more specific, from the sets of the principal component vectors p_(k)(λ) (k=1 to n) for n tile images T_(L) and the corresponding principal component images S_(k) (k=1 to n), the sets of m (m<n) principal component vectors p_(k)(λ) (k=1 to m) and the corresponding principal component images S_(k) (k=1 to m) are chosen and used to determine a composite image G. The error in image information that may be possessed by this composite image G as compared with the original tile images T_(L) is used to determine whether the m principal component vectors p_(k)(λ) (k=1 to m) and the corresponding principal component images S_(k) (k=1 to m) are really optimum principal components.

The principal component vectors p_(k)(λ) for the tile images T_(L) are such that the greater the corresponding eigenvalue u_(k), the greater the contribution for each tile image T_(L). To be more specific, the greater the corresponding eigenvalue u_(K), the greater the contribution of the principal component vectors p_(k)(λ) to the spectral reflectance distribution for the tile images T_(L). Considering this fact, the principal component vectors p_(k)(λ) are lined up in the decreasing order of eigenvalue u_(k) and composite images G are reconstructed under a given illuminating light source by adopting an increasing number of principal component vectors having the larger eigenvalues u_(k). In this process, the error in the image information of the composite image G has with respect to the original tile images T_(L) each composed of n principal component vectors decreases monotonically with the increase in m, or the number of the principal component vectors to be adopted. Therefore, the principal component vector number m at which the above-mentioned error first decreases below a predetermined value can be taken as a minimum optimum principal component number m₁. As a result, the principal component vectors adopted to determine a composite image G with the optimum principal component number m₁ and the principal component images obtained on the basis of these vectors can respectively be extracted as optimum principal component vectors p_(k)(λ) (k=1 to m₁) and optimum principal component images S_(k) (k=1 to m₁) for the tile images T_(L). Such optimum principal component vector p_(k)(λ) (k=1 to m₁) and optimum principal component image S_(k) (k=1 to m₁) are determined for each tile image T_(L).

The term “image information” as used herein means calorimetric values L*, a* and b* as measured in the CIEL*a*b* color space under a given light source such as the CIED₆₅ standard illuminant, and the “error” in such image information is the color difference ΔE₀ expressed by the following equation (1) The optimum principal component m₁ can be determined by finding the principal component image number m at which the color difference ΔE₀ is below a predetermined value, say, 1.0: ΔE ₀={(ΔL*)²+(Δa*)²+(Δb*)²}^(1/2)  (1) where ΔL*, Δa* and Δb* represent the differences the composite image G has with respect to the original tile images T_(L) in terms of the average calorimetric values L*, a* and b* measured for all or part of the tile image. Thus, the optimum principal component number m₁ is adaptively determined on the basis of the color differences ΔE₀ between the calorimetric values of the composite image G in a color space and those of the original image.

The error in the image information may be the square error E₁ from the original image of the spectra for the pixels in all or part of the tile images of the composite image G constructed by the m principal component vectors p_(k). The image data for the spectra having spectral information in correspondence with the bands of the composite image G can also be regarded as an example of colorimetric values and the optimum principal component number m₁ may be adaptively determined on the basis of the square error E₁ between the spectral image data that are the colorimetric values of the composite image G in a color space and the spectral image data which are the calorimetric values of the original image. Since the square error E₁ or log(E₁) decreases monotonically with the principal component vector number m, the optimum principal component number m₁ may be determined by increasing m and finding the value of m at which the amount of decrease in the square error E₁ or log(E₁) becomes smaller than a predetermined value, or a minimum value of m at which the decrease in the square error E with increasing m is saturated below a predetermined value may be substituted.

The obtained tile information consists of the optimum principal component number m₁, optimum principal component vectors p_(k)(λ) (k=1 to m₁) and the optimum principal component images s_(k) (k=1 to m₁) is sent to the recording medium drive unit 24 as compressed image data together with the tile image information that was constructed when the multispectral image was segmented into tile images T_(L) in the image segmenting section 22 b and which consists of the tile number of each tile image T_(L), the tile position on the multispectral image, tile size (image size) of each tile image T_(L) and the pointer (address) for the tile information.

Further, the multispectral image compressing unit 22 is constructed such that over all of the image data the multispectral image is logarithmically converted at a time by the image data converting section 22 a and the thus obtained log-converted image data and the segmentation information into the tile images T_(L) obtained by the image segmenting section 22 b are sent to the principal component analyzing section 22 c, then the log-converted tile image data is obtained by the principal component analyzing section 22 c. However, the present invention is not limited this case, as shown in FIG. 2, the multispectral image compressing unit 22 may be constructed such that the log-converted image data of the multispectral image obtained by the image data converting section 22 a is segmented into the tile images T_(L) and the thus obtained log-converted tile image data of each tile image T_(L) is sent to the principal component analyzing section 22 c, or may be constructed such that the multispectral image is segmented into the tile images T_(L) by the image segmenting section 22 b, then, the thus obtained the image data of each tile image T_(L) is logarithmically converted by the image data converting section 22 a and the thus obtained log-converted tile image data is sent to the principal component analyzing section 22 c.

Having described the configuration of the system 10, we next describe the sequence of steps in the method of the invention for compressing multispectral images by the system 10. The reader of the following description should refer to FIG. 4 for better understanding of the invention.

First, the subject O is recorded with a multiband camera consisting of the light source 12, variable filter 14 and CCD camera 16 to acquire a multiband image which consists of a plurality of band images corresponding to the n bands into which the recording wavelength range has been divided (step 100). The acquired multiband image is temporarily stored in the multiband image data storage unit 18 and then sent to the multispectral image acquisition unit 20.

The multispectral image acquisition unit 20 has one-dimensional look-up tables (1D-LUTs) typically constructed from the correspondence between the image data for gray patches on a Macbeth chart and the values of their spectral reflectance. Using these 1D-LUTs as well as the image data for the multiband image of the subject O that has been retrieved from the multiband image data storage unit 18, the unit 20 estimates the pixel-based spectral sensitivities of the subject (that is photographed) O and acquires the image data for the multispectral image (step 102). To estimate a highly precise spectral reflectance distribution for the subject O, deconvolution may optionally be performed by matrix operations and Fourier transformation.

Next, the image data of the multispectral image is logarithmically converted (step 103). The resultant log-converted tile image data is sent to the principal component analyzing section 22 c. The reason why the image data of the multispectral image is logarithmically converted is as follows:

The image data of the multispectral image is a kind of image data which can be obtained in accordance with spectral transmission characteristics of the variable filter 14 that show a steep mountain-like distribution with a specified peak wavelength being in the center so that the value of the image data of the tile image T_(L) of the kind as described below is approximately shown as a product of a value of intensity distribution of spectral wavelengths of an illuminating light from the light source 12 at the specified peak wavelength above described, a value of spectral reflectance distribution of the subject O and a value of spectral sensitivity characteristics of the CCD camera 16. On this occasion, when the product, namely, the value of image data of the tile image T_(L), is subjected to log conversion, the value of the resultant log-converted image data of the tile image T_(L) is represented as a sum of a logarithmic value of the value of the intensity distribution of the spectral wavelengths of the illuminating light from the light source 12, a logarithmic value of the value of the spectral reflectance distribution of the subject O and a logarithmic value of the value of the spectral sensitivity characteristics of CCD camera 16. Therefore, as shown in a formula (3) to be described below, the resultant sum is allowed to correspond to a linear sum of principal component vectors which is executed in the principal component analysis.

On the other hand, in the case that there exists a portion in which the spectral intensity distribution of the light source 12 is different from other portions even in the subject O having the same spectral reflectance, since the image data of the tile image T_(L) which has not been subjected to log conversion is expressed by the product of the value of the intensity distribution of the spectral wavelength, the value of the spectral reflectance distribution of the subject O and the value of the spectral sensitivity of the CCD camera 16, it can not be expressed in correspondence with the principal analysis that is to be expressed by the linear sum of the principal component vector p_(K)(λ). Accordingly, in this case, the image data of the tile image T_(L) must be expressed by increasing the number of the principal components; hence, compression efficiency at the time of image compression which is the subject of the present invention can not sufficiently be enhanced.

In the next step 104, the multispectral image data is segmented in a tile format to produce tile images T_(L) (L=1 to n_(t)). At the same time, tile image information as header information is constructed and this information comprises the tile number of each tile image T_(L), the tile position on the multispectral image, the tile size (image size) of each tile image T_(L) and the pointer (address) for the tile information (to be described later) which consists of the optimum principal component number m₁, optimum principal component vectors p_(k)(λ) and the image data for the optimum principal component images S_(k).

As FIG. 3 shows, all tile images T_(L) (L=1 to n_(t)) have identical image size from the viewpoint of easy processing. If desired, in order to increase the compression ratio, the size of the tile images T_(L) may be varied as appropriate for the contents of the original image. Segmentation information of the thus obtained tile images T_(L) (L=1˜n_(t)) are sent to the principal component analyzing section 22 c.

It should be noted that the present embodiment logarithmically converts the entire multispectral image data at a time, but the log conversion may first be performed on each of the image data of the tile images T_(L) produced by segmenting the image and then the principal component analysis is performed thereon.

In the next step 106, principal component analysis is performed for each of the log-converted image data of the tile images T_(L) (L=1 to n_(t)) in order to determine principal component images S_(k) (k=1 to n) and principal component vectors p_(k)(λ) (k=1 to n). The process of this principal component analysis is described below.

At the pixel position (i,j), each tile image T_(L) comprises log-converted tile image data including a logarithmic value of a spectral reflectance distribution having n spectral reflectance values. If this log-converted tile image data is written as R(i,j,λ)=[R(i,j,λ₁), R(i,j,λ₂), R(i,j,λ₃), . . . , R(i,j, λ_(n))]^(T) (superscript T represents transposition), calculation is made to determine an autocorrelation matrix T for all pixels in the tile image or part of them, for example, the pixels that are left intact after some pixels are thinned out of whole pixels of the tile image at given intervals.

The thus obtained autocorrelation matrix T is a square (n×n) matrix and can be used to determine eigenvalues u_(k) (u₁>u₂> . . . >u_(n), k=1 to n) and principal component vectors p_(k)(λ) that satisfy the following equation (2). The principal component vectors p_(k)(λ) are orthonormalized eigenvectors and expressed by [p_(k)(i,j,λ₁), p_(k)(i,j,λ₂), p_(k)(i,j,λ₃), . . . , p_(k)(i,j,λ_(n))]^(T) (k=1 to n). Methods for determining eigenvalues and eigenvectors are not limited to any particular types and various known methods may be employed, as exemplified by the Jacobi method and the power method. T·p _(k)(λ)=u _(k) p _(k)(λ)  (2)

As the following equation (3) shows, the log-converted tile image data R(i,j,λ) at the pixel position (i,j) is expressed by the principal component vectors p_(k)(λ) (k=1 to n) which are eigenvectors:

$\begin{matrix} {{R\left( {i,j,\lambda} \right)} = {\sum\limits_{k = 1}^{n}\;{{s_{k}\left( {i,j} \right)}{p_{k}(\lambda)}}}} & (3) \end{matrix}$

Since principal component vectors p_(k)(λ) (k=1 to n) are orthonormal to each other, s_(k)(i,j) can be determined from the following equation (4): s _(k)(i,j)=R(i,j,λ)·p _(k)(λ)  (4) where the dot represents the inner product of n-component vectors for the value of band's spectral reflectance, and s_(k)(i,j) is the quantity representing the magnitude of the kth principal component vector p_(k) contained in the log-converted tile image data R(i,j,λ) of the multispectral image at pixel position (i,j) The value of s_(k)(i,j) is calculated at each pixel position and the kth principal component image S_(k) (k=1 to n) is then determined that has the calculated values of s_(k)(i,j) as the image data at the respective pixel positions.

As already mentioned, the contribution made by the first to nth principal components in the log-converted tile image data R(i,j,λ) decreases with decreasing eigenvalue u_(k) that accompanies each principal component. Hence, to the extent that it holds the image information in an optimum manner, the log-inverted tile image data R(i,j,λ) can be approximated without the principal component vectors p_(k) having smaller eigenvalues u_(k).

This method of approximation is expressed by the following equation (5); the eigenvalues u_(k) (k=1 to n) are lined up in decreasing order and the eigenvectors, that is, the principal component vectors, p_(k)(λ) (k=1 to m) which correspond to the first m eigenvalues u_(k) (k=1 to m) are adopted but the eigenvectors, that is, principal component vectors, p_(k)(λ) (k=m+1 to n) of the smaller eigenvalues are neglected; in this way, the log-converted tile image data R(i,j,λ) is approximated and the image data is compressed accordingly:

$\begin{matrix} {{R\left( {i,j,\lambda} \right)} \approx {\sum\limits_{k = 1}^{n}\;{{s_{k}\left( {i,j} \right)}{p_{k}(\lambda)}}}} & (5) \end{matrix}$

Based on this theory, the optimum principal component number m₁, or the number of principal component vectors p_(k) to be adopted that can approximate the log-converted tile image data R(i,j,λ) without compromising the image information, is identified. As a result, the image data can be compressed without deteriorating the quality of the multispectral image.

In other words, the optimum principal component number m₁ for adopting the principal component vectors p_(k)(λ) (k=1 to m₁) which are eigenvectors of greater eigenvalues u_(k) but neglecting the eigenvectors p_(k)(λ) (k=m₁+1 to n) having smaller eigenvalues is set by the following criterion for decision (step 108).

First, substitute the principal component vectors p_(k)(λ) into Eq. (4) in the decreasing order of eigenvalue u_(k) and determine an approximate log-converted tile image data R′(i,j,λ) which corresponds to the multispectral image, as expressed by the following equation (6):

$\begin{matrix} {{R^{\prime}\left( {i,j,\lambda} \right)} = {\sum\limits_{k = 1}^{m}\;{{s_{i}\left( {i,j} \right)}{p_{i}(\lambda)}}}} & (6) \end{matrix}$

The approximate log-converted tile image data R′(i,j,λ) is not exactly the same as the log-converted tile image R(i,j,λ) and contains some error. However, if this approximate log-converted tile image data R′(i,j,λ) is anti-logarithmically converted and, then, multiplied by a specified spectral intensity distribution to provide image information about the composite image, the error presented in this image information with respect to the image information for the original tile image that is obtained by multiplying the image data of the tile images T_(L) by the above-described spectral intensity distribution decreases with the increasing principal component number m. Considering this fact, a specified value is preliminarily set as the criterion for decision and the minimum optimum principal component number m₁ is acquired by determining the principal component number m at which the error between the image information about the composite image G as obtained by multiplying the approximate log-converted tile image data R′(i,j,λ) after anti-logarithmically converted by the spectral intensity distribution and the image information about the original tile image T_(L) first becomes smaller than the specified value which is set as the criterion for decision.

To give an example, the color differences ΔE₀ of calorimetric values L*, a* and b* in the CIE L*a*b* color space under the CIE D₆₅ standard illuminant are taken as the error between the image information about the composite image G and that about the multispectral image and a specified value is set for each of these color differences ΔE₀ and the minimum optimum principal component number m₁ is determined.

The error may be the square error E₁ of the spectra for all or part of the composite image G. In this case, one may determine a minimum value of the optimum principal component number m₁ at which the decrease in the square error E₁ with increasing principal component number m is saturated below a predetermined value.

Thus determined is the minimum optimum principal component number m₁ which holds and optimally represents the image information about each of the tile images T_(L) and based on this number, one acquires ml optimum principal component vectors p_(k)(λ) (k=1 to m₁) corresponding to eigenvalues u₁ to u_(m1) (u₁ to u_(m1)>u_(m1+1)> . . . >u_(n)), as well as m₁ optimal principal component images S_(k) (k=1 to m₁). In comparatively many cases, the rejected principal component vectors p_(k)(λ) (k=m₁+1 to n) are predominantly composed of the noise component in the tile images T_(L) and, hence, the noise component in the multispectral image can be suppressed by rejecting the less contributing principal component vectors p_(k)(λ) (k=m₁+1 to n) from the tile images T_(L).

Thus, in step 108, the optimum principal component number, optimum principal component vectors p_(k)(λ) and the image data for the optimum principal component images S_(k) are obtained for each of the tile images T_(L) (L=1 to n_(t)) and together with the tile image information constructed in step 104, this tile information is sent to the recording medium drive unit 24 where they are recorded and stored on a recording medium such as hard disk, floppy disk, MO, CD-R or DVD (step 110). In the embodiment under consideration, the multispectral image is segmented into tile images T_(L) and principal component analysis is performed for each tile image T_(L) and this offers a particular advantage in that if the tile images T_(L) do not change greatly in hue, lightness or saturation, the optimum principal component number m₁ suffices to be 1 or 2 and this means that only one or two optimum principal component vectors p_(k) are necessary to hold and optimally represent the image information. As a result, the image data for the multispectral image can be compressed at a higher ratio without sacrificing the quality of the image.

In particular, as described above, the log-converted tile image data R(i,j,λ) produced by logarithmically converting the image data of the tile images T_(L) is expressed by the sum of the logarithmic value of the spectral reflectance distribution of the subject O and the logarithmic value of the intensity distribution of the spectral wavelengths so that, when there exists a portion in which the illumination intensity of the light source 12 is different while the spectral reflectance of the subject O is same, for example, when there exists a shaded portion caused by the illuminating light on a surface of the same material of the subject O, the log-converted tile image data R(i,j,λ) comes to be a sum of the principal component vectors p_(k)(λ) of the log-converted image data of the spectral reflectance distribution of the same material of the subject O and the log-converted bias quantity of the spectral intensity distribution in the shaded portion caused by the illuminating light. For this reason, in a principal component analysis method which makes expression by means of the linear sum of the principal component vectors, principal components based on the spectral reflectance of the subject O can effectively be extracted in log-converted form discriminating from the bias quantity by the spectral intensity distribution of the illuminating light. As a result, the optimum principal component number m₁ can be brought to be small as compared with the case in which the principal component analysis is performed without undergoing the log conversion whereupon the ratio of the image compression can be enhanced, which is the object of the present invention.

After determining the optimum principal component number m₁ and acquiring both the optimum principal component vectors p_(k)(λ) (k=1 to m₁) and the optimum principal component images S_(k) (k=1 to m₁) in step 108, the optimum principal component images S_(k) (k=1 to m₁) may optionally be subjected to further image compression by a suitable technique such as the JPEG (Joint Photographic Experts Group) technique, or compressed to an even higher ratio by Huffman coding or any other known arithmetic coding techniques.

The image data stored in recording media or the like after having been compressed is read, if needed, and, based on the thus read image data, the optimum principal component vectors p_(k)(λ) (_(k)=1˜m₁), the optimum principal component images S_(k) (S_(k)=1˜m1) and tile image information such as tile number, tile position and tile size are obtained and, further, based on the optimum principal component vectors p_(k)(λ) and the optimum principal component images S_(k), an approximate log-converted image data R′(i,j,λ) is obtained for each tile image T_(L) and, as a last step, anti-logarithmic conversion is performed to obtain the image data of the tile images T_(L) and, finally, the image data of one multispectral image are determined.

Using the above-described multispectral image compressing method and apparatus, the present inventors made an experiment of multispectral image compression in the following manner.

CA-D4-1024A of DALSA (1024×1024 pics each sized to 12×12 microns; furnished with PCI interface; monochromatic) was used as CCD camera 16, and Varispec Tunable Filter (liquid-crystal tunable filter of CRI) as variable filter 14. By means of this apparatus, the recording wavelength range of from 380 to 780 nm was divided by 10 nm into 41 bands (n=41). The subject O was a person standing outdoors who was recorded with the CCD camera 16 to take a multiband image of the person consisting of 41 band images.

The multiband image storage section 18, multispectral image acquisition unit 20 and multispectral image compressing unit 22 were implemented in a notebook PC (personal computer) of PROSIDE and processing with software was executed on Windows 95 in C⁺⁺. The CPU in the PC ran at 166 MHz and the RAM had a capacity of 128 Mbytes.

To provide convenience in processing with software, the image data quantization number was changed from 2 bytes to 1 byte in a preliminary treatment, which was not included in the process of compressing the volume of image data to be described below.

First, a multispectral image having 41 constituent wavelength bands was extracted from the multiband image that had been sent to the multispectral image acquisition unit 20. The extracted multispectral image consisting of 1024×1024 pixels was segmented into tile images T_(L) (L=1 to 4096) each consisting of 16×16 pixels.

Principal component analysis was performed each of the tile images T_(L) (L=1 to 4096) to determine principal component vectors p_(k)(λ) (k=1 to 41) and principal component images S_(k) (k=1 to 41).

Then, in order to determine the optimum principal component number m₁, a value of 1.5 in terms of the average color difference based on chromaticity in the CIE D1976 L*a*b* color space under the CIE D₆₅ standard illuminant was chosen as the criterion for decision. In the decreasing order of eigenvalues u_(k) obtained by the principal component analysis, the m corresponding principal component vectors p_(k)(λ) (k=1 to m) were sequentially adopted. After reconstructing a composite G image on the basis of these principal component vectors p_(k), the above-defined average color difference from the original image composed of the tile images T_(L) was determined and an optimum principal component m₁ was also determined at which the average color difference was below 1.5.

As it turned out, the optimum principal component number m₁ for the tile images T_(L) (L=1 to 4096) was between 2 and 6. In the case of the tile image T_(B) shown in FIG. 3 which is in a blank area that has small changes in hue, lightness and saturation and which is part of the background of the human subject O or in the case of a tile image T_(L) having identical hue, lightness and saturation, the optimum principal component number m₁ was smaller than in the case of the tile image T_(A) also shown in FIG. 3 and a value of 2 was sufficient; in other words, the tile images T_(L) could be expressed by the first principal component vector p₁(λ) and the second principal component vector p₂(λ). When a less stringent criterion was used, the first principal component vector p₁(λ) was sufficient to hold and represent the original image information in an optimal way.

In the next step, tile image information is added to the optimum principal component number m₁, optimum principal component vectors p_(k)(λ) and optimum principal component images S_(k) for each tile image T_(L) and the tile image information consists of the tile number of tile image T_(L), the tile position on the multispectral image, the tile size (image size) of tile image T_(L) and the pointer (address) for the tile information consisting of said optimum principal component number m₁, optimum principal component vectors p_(k)(λ) and the image data for the optimum principal component images S_(k). The combined data is recorded and stored via the recording medium drive unit 24.

As a result, the image data was reduced from about 41 Mbytes to 3.5 Mbytes, which was about one twelfth the initial volume. The compressed image data was later retrieved and an image was reconstructed but no visual deterioration in image quality was observed.

Obviously, it is clear that the image compressing method according to the first aspect of the invention and the image compressing apparatus using it can compress a plurality of spectral images at a higher ratio, say, about 12:1 without causing any visual deterioration and thus contribute to more efficient handling of image data.

While the multispectral image compressing method and apparatus of the invention have been described above in detail, it should be noted that the foregoing embodiment is not the sole case of the invention and various improvements and modifications are possible without departing from the scope and spirit of the invention.

As described above in detail, according to the present invention, a multispectral image of high data volume or large capacity is segmented into a plurality of tile images and the principal component analysis is performed using logarithmically converted image data of the tile images such that they conform with the principal component analysis method which makes expression by means of a linear sum of the principal component vectors whereupon a high image compression ratio can be achieved without deteriorating the image quality and the efficiency of image data handling can be improved.

As a further advantage, the principal component vectors based on the tile images can be freed of those portions in which the noise component is predominant and this enables suppression of the noise component. 

1. A method for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising the steps of: performing logarithmic conversion of said multispectral image and segmentation of said multispectral image into a plurality of tile images to obtain logarithmically converted tile image data; performing principal component analysis on said logarithmically converted tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image; determining from said plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image; and expressing compressed image data for said multispectral image by means of at least said optimum principal component number of sets of optimum principal component images and optimum principal component vectors for each tile image, and by means of tile image information having information about tile numbers of said tile images, a tile position and an image size of said tile images.
 2. The method according to claim 1, wherein said logarithmically converted tile image data is obtained by segmenting said multispectral image into said plurality of tile images and subjecting image data of each of said plurality of tile images to logarithmic conversion.
 3. The method according to claim 1, wherein said logarithmically converted tile image data is obtained by subjecting said multispectral image to logarithmic conversion to obtain logarithmically converted image data of said multispectral image and segmenting said multispectral image into said plurality of tile images based on said logarithmically converted image data.
 4. The method according to claim 1, wherein said logarithmically converted tile image data is determined based on logarithmically converted image data of said multispectral image obtained by subjecting said multispectral image to logarithmic conversion and segmentation information for segmenting said multispectral image into a plurality of tile images.
 5. The method according to claim 1, wherein said optimum principal component number is determined based on colorimetric values in a color space.
 6. The method according to claim 1, wherein said optimum principal component number is a minimum principal component number at which an absolute value of difference between image information about a composite image that is composed of selected number of sets of said principal component vectors and said principal component images and the image information about an original image that is composed based on said multispectral image is below a specified value.
 7. The method according to claim 1, wherein said optimum principal component number is a minimum principal component number at which an absolute value of variation in error from an original image that is composed based on said multispectral image which is presented when a composite image that is composed of selected number of sets of said principal component vectors and said principal component images is determined by sequentially including in an order of contribution those principal component vectors which make greater contribution to said multispectral image does not exceed a predetermined value.
 8. The method according to claim 1, wherein an image size of said tile images in terms of pixel is expressed as a power notation of 2 in both a longitudinal and a transverse direction.
 9. The method according to claim 1, wherein said tile images all have an image size in terms of pixel.
 10. An apparatus for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising: an image segmenting section for segmenting said multispectral image or a logarithmically converted multispectral image into a plurality of tile images, or obtaining segmentation information for segmenting said multispectral image into said plurality of tile images; an image data converting section which logarithmically converts image data of said multispectral image or said plurality of tile images obtained by said image segmenting section; a principal component analyzing section in which respective logarithmically converted tile image data obtained via said image segmenting section and said image data converting section are subjected for each tile image to principal component analysis to obtain a principal component number of sets of principal component vectors and principal component images for the multispectral image; and an optimum principal component vector/image extracting section which determines an optimum principal component number of sets of optimum principal component vectors and optimum principal component images corresponding to the principal component number of sets of principal component vectors and principal component images that optimally represent image information about the multispectral image from said principal component number of sets of principal component vectors and principal component images as obtained in the principal component analyzing section; and wherein image data for said multispectral image is compressed by expressing by means of at least the optimum principal component vectors and the image data for the optimum principal component images for each tile image as obtained in said optimum principal component vector/image extracting section, and by means of tile image information having information about tile numbers of said tile images, a tile position and an image size of said tile images.
 11. The apparatus according to claim 10, wherein said image segmenting section segments said multispectral image into said plurality of tile images and said image data converting section converts logarithmically said image data of said plurality of tile images obtained by said image segmenting section to obtain said logarithmically converted tile image data.
 12. The apparatus according to claim 10, wherein said image data converting section converts logarithmically said image data of said multispectral image to obtain logarithmically converted image data of said multispectaral image and said image segmenting section segments logarithmically converted multispectral image into said plurality of tile images based on said logarithmically converted image data of said multispectral image to obtain said logarithmically converted tile image data.
 13. The apparatus according to claim 10, wherein said image segmenting section obtains segmentation information for segmenting said multispectral image into said plurality of tile images, said image data converting section converts logarithmically said image data of said multispectral image to obtain logarithmically converted image data of said multispectral image and said principal component analyzing section determines said logarithmically converted tile image data based on said segmentation information obtained by said image segmenting section and said logarithmically converted image data of said multispectral image obtained by said image data converting section before said principal component analysis.
 14. A method for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising the steps of: performing logarithmic conversion of said multispectral image and segmentation of said multispectral image into a plurality of tile images to obtain logarithmically converted tile image data; performing principal component analysis on said logarithmically converted tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image; determining from said plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image; and expressing compressed image data for said multispectral image by means of at least said optimum principal component number of sets of optimum principal component images and optimum principal component vectors for each tile image, wherein said optimum principal component number is determined based on colorimetric values in a color space.
 15. A method for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising the steps of: performing logarithmic conversion of said multispectral image and segmentation of said multispectral image into a plurality of tile images to obtain logarithmically convened tile image data; performing principal component analysis on said logarithmically convened tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image; determining from said plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image; and expressing compressed image data for said multispectral image by means of at least said optimum principal component number of sets of optimum principal component images and optimum principal component vectors for each tile image, wherein said optimum principal component number is a minimum principal component number at which an absolute value of difference between image information about a composite image that is composed of selected number of sets of said principal component vectors and said principal component images and the image information about an original image that is composed based on said multispectral image is below a specified value.
 16. A method for compressing a multispectral image obtained by using band images of a subject captured in a wavelength range divided into a plurality of bands, comprising the steps of: performing logarithmic conversion of said multispectral image and segmentation of said multispectral image into a plurality of tile images to obtain logarithmically converted tile image data; performing principal component analysis on said logarithmically converted tile image data of respective tile images to obtain for each tile image a principal component number of sets of principal component vectors and principal component images based on the multispectral image; determining from said plurality of sets, for each tile image, an optimum principal component number of sets of optimum principal component vectors and corresponding optimum principal component images that optimally represent image information about the multispectral image; and expressing compressed image data for said multispectral image by means of at least said optimum principal component number of sets of optimum principal component images and optimum principal component vectors for each tile image, wherein said optimum principal component number is a minimum principal component number at which an absolute value of variation in error from an original image that is composed based on said multispectral image which is presented when a composite image that is composed of selected number of sets of said principal component vectors and said principal component images is determined by sequentially including in an order of contribution those principal component vectors which make greater contribution to said multispectral image does not exceed a predetermined value. 